Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/2435#issuecomment-55972221
Each row is a single (random) dataset. The 2 different sets of result
columns are for 2 different RF implementations:
* (numTrees): This is from an earlier commit, after implementing
RandomForest to train multiple trees at once. It does not include any code for
feature subsampling.
* (feature subsets): This is from this current PR's code, after
implementing feature subsampling.
These tests were to identify regressions in DecisionTree, so they are
training 1 tree with all of the features (i.e., no feature subsampling).
I have run other tests with numTrees=10 and with sqrt(numFeatures), and
those indicate that multi-model training and feature subsets can speed up
training for forests.
(I'll update the description with this clarification.)
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